Multilevel Image Threshold Estimation using Teaching Learning-based Optimization

doi:10.23940/ijpe.21.07.p8.638646

Abstract

Abstract: With a rapid expansion of image segmentation over the past decades, the growth of the mathematical optimization in the form of image thresholding has been enormous on segmentation. A need to organize image thresholding has risen in order to help medical imaging, detection, and recognition in making informed decisions about the images. Image thresholding based on soft computing approaches are used online to cluster the medical imaging into positive or negative diagnoses. The proposed teaching learning based optimization (TLBO) is relied upon maximizing between class variance. Different from previous optimization techniques, TLBO has been utilized as a prime optimization method and its execution is straightforward including less computational exertion. This technique has been tried on standard benchmark test images and sample images with the increase in threshold. Numerical outcomes on examination show that this method is a promising option for the multilevel image thresholding issue.

Key words: image segmentation, multilevel thresholding, soft computing, teaching learning based optimization, between-class variance

S. Anbazhagan, and S. Karthikumar. Multilevel Image Threshold Estimation using Teaching Learning-based Optimization [J]. Int J Performability Eng, 2021, 17(7): 638-646.

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